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Title: Learning Sparse Nonparametric DAGs
We develop a framework for learning sparse nonparametric directed acyclic graphs (DAGs) from data. Our approach is based on a recent algebraic characterization of DAGs that led to a fully continuous program for scorebased learning of DAG models parametrized by a linear structural equation model (SEM). We extend this algebraic characterization to nonparametric SEM by leveraging nonparametric sparsity based on partial derivatives, resulting in a continuous optimization problem that can be applied to a variety of nonparametric and semiparametric models including GLMs, additive noise models, and index models as special cases. Unlike existing approaches that require specific modeling choices, loss functions, or algorithms, we present a completely general framework that can be applied to general nonlinear models (e.g. without additive noise), general differentiable loss functions, and generic black-box optimization routines.
Authors:
; ; ; ;
Award ID(s):
1909816
Publication Date:
NSF-PAR ID:
10197055
Journal Name:
Proceedings of Machine Learning Research
Volume:
108
Page Range or eLocation-ID:
3414-3425
ISSN:
2640-3498
Sponsoring Org:
National Science Foundation
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